6,434 research outputs found
Surrogate modeling of thermodynamic equilibria: applications, sampling and optimization
Models based on first principles are an effective way to model chemical processes. The quality of these depends critically on the accurate description of thermodynamic equilibria. This is provided by modern thermodynamic models, e.g., PC-SAFT, but they come with a high computational cost, which makes process optimization challenging. This can be addressed by using surrogate models to approximate the equilibrium calculations. A high accuracy of the surrogate model can be achieved by carefully choosing the points at which the original function is evaluated to create data for the training of the surrogate models, called sampling. Using a case study, different approaches to sampling are discussed and evaluated with a focus on new approaches to adaptive sampling
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Computational Methods for Parameter Estimation in Climate Models
Intensive computational methods have been used by Earth scientists in a wide range of problems in data inversion and uncertainty quantification such as earthquake epicenter location and climate projections. To quantify the uncertainties resulting from a range of plausible model configurations it is necessary to estimate a multidimensional probability distribution. The computational cost of estimating these distributions for geoscience applications is impractical using traditional methods such as Metropolis/Gibbs algorithms as simulation costs limit the number of experiments that can be obtained reasonably. Several alternate sampling strategies have been proposed that could improve on the sampling efficiency including Multiple Very Fast Simulated Annealing (MVFSA) and Adaptive Metropolis algorithms. The performance of these proposed sampling strategies are evaluated with a surrogate climate model that is able to approximate the noise and response behavior of a realistic atmospheric general circulation model (AGCM). The surrogate model is fast enough that its evaluation can be embedded in these Monte Carlo algorithms. We show that adaptive methods can be superior to MVFSA to approximate the known posterior distribution with fewer forward evaluations. However the adaptive methods can also be limited by inadequate sample mixing. The Single Component and Delayed Rejection Adaptive Metropolis algorithms were found to resolve these limitations, although challenges remain to approximating multi-modal distributions. The results show that these advanced methods of statistical inference can provide practical solutions to the climate model calibration problem and challenges in quantifying climate projection uncertainties. The computational methods would also be useful to problems outside climate prediction, particularly those where sampling is limited by availability of computational resources.National Science Foundation OCE-0415251CONACyT-Mexico 159764Institute for Geophysic
Predictive Scale-Bridging Simulations through Active Learning
Throughout computational science, there is a growing need to utilize the
continual improvements in raw computational horsepower to achieve greater
physical fidelity through scale-bridging over brute-force increases in the
number of mesh elements. For instance, quantitative predictions of transport in
nanoporous media, critical to hydrocarbon extraction from tight shale
formations, are impossible without accounting for molecular-level interactions.
Similarly, inertial confinement fusion simulations rely on numerical diffusion
to simulate molecular effects such as non-local transport and mixing without
truly accounting for molecular interactions. With these two disparate
applications in mind, we develop a novel capability which uses an active
learning approach to optimize the use of local fine-scale simulations for
informing coarse-scale hydrodynamics. Our approach addresses three challenges:
forecasting continuum coarse-scale trajectory to speculatively execute new
fine-scale molecular dynamics calculations, dynamically updating coarse-scale
from fine-scale calculations, and quantifying uncertainty in neural network
models
A surrogate accelerated multicanonical Monte Carlo method for uncertainty quantification
In this work we consider a class of uncertainty quantification problems where
the system performance or reliability is characterized by a scalar parameter
. The performance parameter is random due to the presence of various
sources of uncertainty in the system, and our goal is to estimate the
probability density function (PDF) of . We propose to use the multicanonical
Monte Carlo (MMC) method, a special type of adaptive importance sampling
algorithm, to compute the PDF of interest. Moreover, we develop an adaptive
algorithm to construct local Gaussian process surrogates to further accelerate
the MMC iterations. With numerical examples we demonstrate that the proposed
method can achieve several orders of magnitudes of speedup over the standard
Monte Carlo method
Adaptive physics-informed neural operator for coarse-grained non-equilibrium flows
This work proposes a new machine learning (ML)-based paradigm aiming to
enhance the computational efficiency of non-equilibrium reacting flow
simulations while ensuring compliance with the underlying physics. The
framework combines dimensionality reduction and neural operators through a
hierarchical and adaptive deep learning strategy to learn the solution of
multi-scale coarse-grained governing equations for chemical kinetics. The
proposed surrogate's architecture is structured as a tree, with leaf nodes
representing separate neural operator blocks where physics is embedded in the
form of multiple soft and hard constraints. The hierarchical attribute has two
advantages: i) It allows the simplification of the training phase via transfer
learning, starting from the slowest temporal scales; ii) It accelerates the
prediction step by enabling adaptivity as the surrogate's evaluation is limited
to the necessary leaf nodes based on the local degree of non-equilibrium of the
gas. The model is applied to the study of chemical kinetics relevant for
application to hypersonic flight, and it is tested here on pure oxygen gas
mixtures. In 0-D scenarios, the proposed ML framework can adaptively predict
the dynamics of almost thirty species with a maximum relative error of 4.5% for
a wide range of initial conditions. Furthermore, when employed in 1-D shock
simulations, the approach shows accuracy ranging from 1% to 4.5% and a speedup
of one order of magnitude compared to conventional implicit schemes employed in
an operator-splitting integration framework. Given the results presented in the
paper, this work lays the foundation for constructing an efficient ML-based
surrogate coupled with reactive Navier-Stokes solvers for accurately
characterizing non-equilibrium phenomena in multi-dimensional computational
fluid dynamics simulations
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